# Achievable Information Rates for Fiber Optics: Applications and Computations

arXiv: https://arxiv.org/abs/1709.10393 · HTML: https://mlsanity.com/paper/1709.10393/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2017.

## Built on by (most cited first, 6 of 6)

- [Low-complexity Near-optimum Symbol Detection Based on Neural Enhancement of Factor Graphs](https://mlsanity.com/paper/2203.16417/) (2022)
- [Blind Channel Estimation and Joint Symbol Detection with Data-Driven Factor Graphs](https://mlsanity.com/paper/2401.12627/) (2024)
- [Neural Enhancement of Factor Graph-based Symbol Detection](https://mlsanity.com/paper/2203.03333/) (2022)
- [Geometric Constellation Shaping for Fiber-Optic Channels via End-to-End Learning](https://mlsanity.com/paper/2211.04311/) (2022)
- [Local Message Passing on Frustrated Systems](https://mlsanity.com/paper/2306.01494/) (2023)
- [Optimization of Iterative Blind Detection based on Expectation Maximization and Belief Propagation](https://mlsanity.com/paper/2408.02312/) (2024)

## Abstract (arXiv)

In this paper, achievable information rates (AIR) for fiber optical communications are discussed. It is shown that AIRs such as the mutual information and generalized mutual information are good design metrics for coded optical systems. The theoretical predictions of AIRs are compared to the performance of modern codes including low-parity density check (LDPC) and polar codes. Two different computation methods for these AIRs are also discussed: Monte-Carlo integration and Gauss-Hermite quadrature. Closed-form ready-to-use approximations for such computations are provided for arbitrary constellations and the multidimensional AWGN channel. The computation of AIRs in optical experiments and simulations is also discussed.